1. ATLAST Deployment &Push Pack Spares Optimizer
Presented by
Dr. Naaman Gurvitz

2. Configurations/Status
ATLAST Model Logic
Time-based Forecasts
Model Input Data
Log Functions
Legend
System
Configurations/Status
Aircraft/Spares
Assembly Location/Age/Status
Availability
Operations
Achieved Flying
Hours
Failures
and Life Limit Events
Assembly Ages
And Repair History
Flying Program
Supply
Stock
Levels
Spares
Unavailability
Spare Pre-positioning
Spares Locations
Engineering
ARDEC
Maintenance
Depot & Field
Deployment
PM/Readiness
Logistics/Supply
IMMC/Item Mgr
Depot Maintenance
Base
Maintenance
Repair
Turn Around
Repair Locations,
Rules/DOF, Capacity,
Cycle Time
Awaiting Parts
Occurrences
Awaiting Maintenance
Arrivals to Depot
Items
Condemned
Tasks
Performed
Order Lead Times
Shipment Times

3. ATLAST Strengths & Applications
Real-World Complexities (State-based Forecasting)
Supports Initialization Down to Serial Number
Initialized “State” of Components
Phased Aircraft Inductions by Tail Number
Aging Analysis
Recap, Zero Aging Analysis
Distribution Swapping based on Repair Count
Reconfigurable Indenture Structures
Life Limit Event Management
Op Temp Variations by Location
Interchangeability/Substitutability
Capacity Constraints
Time-Dependent Outputs
Analysis to be Supported with Model
Maintenance Concept Modifications (e.g. Recapitalization)
Aircraft Induction Strategies
Operations Surge
Parts Purging
Sparing Strategies
Deployment Planning

4. ATLAST Outputs Maintenance Readiness Supply Cost
Removals (Causal, Timed, Opportunistic)
Modules to Depot
Condemnations
Tasks Performed
% of Time Awaiting Maintenance
Avg. Time Awaiting Maintenance
Cost
O&S
O&S Per Flight Hour
Readiness
Time on Wing
Availability
Achieved Flying Hours
Cumulative Age
Supply
% of Time Awaiting Parts
Avg. Time Awaiting Parts
Spares Stock Levels
Spares Unavailability
By Location and Time Dependent

5. ATLAST Optimizer Deployment & Push Pack Spares Optimization Module:
Creates a full deployment scenario on any number of aircraft at a single base
User defines logistics delay and operational profiles
Generates availability vs. cost graph
Provides analyst necessary information to determine optimal spares pack

6. Scenario Setup Process

7. Scenario From Master Database
Select Master Database
Choose Master
Choose Deployment Scenario name OR
Type Location
Select Base
Select aircraft tail numbers

8. Scenario From Master Database
Define Deployment Period
Assign Operational Profile

9. Scenario From Master Database
Set Logistic Delays

10. Scenario From Master Database
Set Number
of Histories
Type
Descriptive
Information

11. Simulation & Optimization
Run Simulation Verifications
Select Ranking Method
Ranked Spares List associated with Cost and Estimated Performance
Select Sparing Strategy and Run Simulation Verification

12. Simulation & Optimization
Selected Sparing Strategy
Blue Points Simulated Values
Red Line Estimated Values
Selected Point
“co-ordinates”

13. Validation 5 Aircraft in 8 quarters Identical Assumptions in
Simulation and Analytical Models
5 Aircraft in 8 quarters
a) No Life Limits
b) Single exponential failure distributions
c) New aircraft

14. Numerical Results: Case A
10 Aircraft in 8 quarters
a) Life Limits
b) Multiple Weibull failure distributions
c) Initial ages

15. Numerical Results: Case B
5 Aircraft in 4 quarters
a) Life Limits
b) Multiple Weibull failure distributions
c) Initial ages

16. Numerical Results: Case C
1 Aircraft in 1 quarter
a) Life Limits
b) Multiple Weibull failure distributions
c) Initial ages

17. Conclusions
AT LAST Deployment Push Pack Spares Optimization Module is both accurate and efficient
Further developments will include:
An automatic adjustment mechanism
Improved handling of “coinciding” LRU removals occurrences
Importing optimization results to the master database

18. Thank You

19. Backup Slides

20. Mathematical Formulation
Markov Process States
Legend
n=number of systems
m=number of LRU’s per system
s= number of spares
= effective failure rate
= repair turnaround rate
Same failure rates (enough spares to ‘fill up’ for failed units)
Reducing failure rates (function of operational units)
Repair rates function of number of failed units
AT-LAST – Finite Markov State Process
Constant failure rates (independent of number of failures)
Repair rates function of number of failed units
Traditional – Infinite Markov State Process

21. Steady State Markov Equations
State s
State s+1
State s+nm
Boundary Condition
State 0
State s
State s+nm
Boundary Condition

22. Steady State Probabilities
Poisson Distribution

23. Little Theorem
Where:
Where:

24. Unavailability Estimates
ATLAST Expression: Applicable for serial systems in which LRU’s do not fail while the system is down
Where:
calculated from outputs